WP2: Large area mapping
Context
Remote sensing based information on land use and land cover change (LULCC) is required at spatial resolutions higher than those currently available from existing global land cover products (Fritz et al., 2011). This is because many LULCC processes, such as logging, deforestation, land abandonment or urban sprawl, represent critical drivers of global environmental change, but occur at spatial scales that cannot be resolved with coarse resolution data in many areas of the world. Despite large amounts of Earth Observation (EO) data available at spatial resolutions of 20 to 50m, land cover and LULCC products with such spatial detail are commonly not available across large areas (Fritz et al., 2011). Image compositing offers opportunities to overcome restrictions in data availability and to improve large area mapping and monitoring at the same time.
Main results
Landsat compositing
Deriving information over large areas for Landsat data still faces numerous challenges. Image compositing (Figure 1) offers great potential to circumvent such shortcomings. We here present a compositing algorithm that facilitates creating cloud free, seasonally and radiometrically consistent datasets from the Landsat archive. A parametric weighting scheme allows for flexibly utilizing different pixel characteristics for optimized compositing. We describe in detail the development of three parameter decision functions: acquisition year, day of year and distance to clouds. Our test site covers 42 Landsat footprints in Eastern Europe (Figure 1) and we produced three annual composites.
Figure 1: The study region boundaries (red), the Carpathian ecoregion (green), the national borders (black) overlaid on the best observation image composite for the target year 2005 (RGB = 4, 5, 3). A total of 1407 scenes were provided to the compositing algorithm, which was parameterized to produce a cloud free, leaf-on seasonal state composite, considering imagery from 2003 to 2007.
We evaluated seasonal and annual consistency and compared our composites to BRDF normalized MODIS reflectance products. Finally, we also evaluated how well the composites work for land cover mapping. Results prove that our algorithm allows for creating seasonally consistent large area composites (Figure 1). Radiometric correspondence to MODIS was high (up to R2 > 0.8), but varied with land cover configuration and selected image acquisition dates. Land cover mapping yielded promising results (overall accuracy 72%). Class delineations were regionally consistent with minimal effort for training data. Class specific accuracies increased considerably (~10%) when spectral metrics were incorporated. Our study highlights the value of compositing in general and for Landsat data in particular, allowing for regional to global LULCC mapping at high spatial resolutions.
Additionnal information can be found in:
Griffiths, P., van der Linden, S., Kuemmerle, T., & Hostert, P. (2013). A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping. IEEE Journal of Selected topics in Applied Earth Observations and RemoteSensing, PP, 1-14.